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Found 4 papers, 1 papers with code
Jet Substructure from Dark Sector Showers
We examine the robustness of collider phenomenology predictions for a dark sector scenario with QCD-like properties.
High Energy Physics - Phenomenology
Cataloging Accreted Stars within Gaia DR2 using Deep Learning
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.
What is the Machine Learning?
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency.
(Machine) Learning to Do More with Less
In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training.
